Alzheimer?s disease and Alzheimer?s disease related dementias (AD/ADRD) are age-associated neurodegenerative diseases that are reaching epidemic proportions. Progression of AD is characterized by losses in memory, orientation, independent decision-making capacity, and self-care. Gains in understanding AD pathogenesis have not yet translated into pharmacological therapies that effectively slow or halt disease progression. Evidence-based behavioral approaches are rapidly becoming recognized as methods to provide effective neurocognitive and therapeutic support for AD/ADRD patients and their caregivers1. Behavioral approaches such as lifestyle changes and risk reduction are non-pharmacological therapies that are accessible, personalizable, have no side effects, and are low in cost. To that end, we are developing mobile device software that is patient and caregiver centered, and provides behavioral-based assistance through visual mapping. The MapHabitTM system (MHS) uses pictures and keywords to assist memory- impaired patients and caregivers in organizing and successfully accomplishing their activities of daily living. This approach is innovative through its unique recruitment of the brain?s habit learning system (neostriatum) rather than the hippocampal structures damaged in AD. Preliminary work revealed that commercially available visual mapping software is too complicated for memory-impaired and technology-nave individuals to use effectively. Commercially available software is proprietary and cannot be modified to meet their needs. In this Phase 1 SBIR application, we propose to further develop and enhance MHS by integrating three novel specific aims that involve (1) development of adaptive user interfaces which can be personalized and dynamically adjusted for cognitive status, allowing for a greater range of memory-impaired individuals to benefit from visual mapping; (2) linkage of personalized visual maps to smart devices, including wearables (e.g., Apple iWatch) and audio interfaces (e.g., Amazon Echo); (3) establishment of a predictive analytics tool that will accurately track and predict changes in functional status. We are advantaged in this SBIR Phase 1 application by having access to patients and caregivers, including underrepresented minority populations, who are currently involved in our preliminary studies assessing the impact of visual mapping on quality of life measures. All of these individuals are already well- characterized in terms of their cognitive and emotional behavior, both before and after the use of visual mapping (see letters of support from Dr. E. Vaughn, Atlanta VA Health Care System, Dr. M. Parker, Emory University Alzheimer?s Disease Research Center, and F. Boatman, RN, Speak Life Management). Those studies will contribute to the preliminary data section of our planned SBIR Phase 2 application that will: assess the effectiveness of MHS on a broad range of large clinical populations, improve the user-experience for memory-impaired individuals, and refine our methods of machine learning to predict healthcare outcomes.